Interested in this AI/ML Engineer role at Notable?
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About This Role
Notable is the leading healthcare AI platform for transforming workforce productivity. Health systems, hospitals, and payers use Notable to improve healthcare quality, close gaps in patient care, drive member enrollment, and patient acquisition, retention, and reimbursement, scaling growth without hiring more staff.
We are on a mission to improve the lives of patients, staff, and clinicians \- to improve healthcare for humanity. This isn't just a lofty goal \- it's something we're achieving every single day. When you join Notable, you become part of a force actively transforming healthcare. Our aim to impact 100 million patients isn't just a number; it's a commitment to creating meaningful change on a massive scale.
Therefore, our culture is purposeful in pursuit of this mission. We believe our culture gives each person the opportunity to do the best work of their lives, work with the best teammates, and have fun achieving great things together.
Role Summary:
Notable builds AI\-driven automation for healthcare. As a Senior AI Platform Engineer, you will design, build, and maintain LLM integrations that power AI features across Notable’s solutions. You’ll translate ambiguous problem statements into clear, high‑quality technical plans and own delivery end‑to‑end — from ideation and requirements, to implementation, launch, and post‑delivery monitoring — with a focus on scalability, reliability, and measurable impact for our customers. You’ll also leverage AI agents to increase day‑to‑day efficiency and share learnings that raise the overall quality bar across the engineering environment.
What You’ll Do:
- Develop and maintain LLM integrations to power AI features across solutions.
- Ensure scalability, reliability, and performance of AI features in production.
- Translate abstract requirements into structured, sound technical plans and milestones.
- Own implementations end‑to‑end: discovery/requirements design build launch post‑delivery monitoring/iterating.
- Evaluate and articulate implications and trade‑offs of technical choices.
- Leverage AI agents to improve development velocity and operational efficiency.
- Collaborate across engineering and adjacent teams to share learnings, improve processes, and continuously raise quality.
You’re a Great Fit if:
- Strong proficiency in Python for production software.
- Proficiency with Jupyter Notebook or an equivalent environment (e.g., JupyterLab, Databricks, Colab, etc.).
- Demonstrated experience building, integrating, and operating LLM‑powered features/services.
- Ability to decompose ambiguous problems, write clear technical plans, and execute with high ownership.
- Experience designing for reliability, scalability, and observability in production systems.
- You leverage AI Agents for day\-to\-day efficiency.
Nice to Have:
- Terraform and Helm Charts for infrastructure and deployment.
- Google Cloud Platform (e.g., GKE, Cloud Run, Cloud Storage).
- Typescript for service or UI integrations.
- Postgres for application data modeling and performance.
- Experience with ML/AI platforms, agents, or orchestration frameworks.
\#LI\-TD1
We value in\-person collaboration and connection. For Bay Area–based employees, this role requires being in our San Mateo office at least three days a week. For remote employees, occasional travel to headquarters is expected for company\-wide events and onsite gatherings.
Beware of job scam fraudsters! Our recruiters use @notablehealth.com email addresses exclusively. We do not conduct interviews via text or instant message, to purchase equipment through us, or to provide sensitive personally identifiable information such as bank account or social security numbers. If you have been contacted by someone claiming to be a recruiter from Notable from a different domain about a job offer, please report it as potential job fraud to law enforcement and contact us here.
Compensation Range: $170K \- $205K
Salary Context
This $170K-$205K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Notable, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $170K to $205K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Notable AI Hiring
Notable has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Mateo, CA, US, Remote, US. Compensation range: $185K - $205K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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